Campus Units
Genetics, Development and Cell Biology, Bioinformatics and Computational Biology, Computer Science
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
6-2004
Journal or Book Title
Neural Computing & Applications
Volume
13
Issue
2
First Page
123
Last Page
129
DOI
10.1007/s00521-004-0414-3
Abstract
In this paper, we describe a machine learning approach for sequence-based prediction of proteinprotein interaction sites. A support vector machine (SVM) classifier was trained to predict whether or not a surface residue is an interface residue (i.e., is located in the protein-protein interaction surface), based on the identity of the target residue and its ten sequence neighbors. Separate classifiers were trained on proteins from two categories of complexes, antibody-antigen and protease-inhibitor. The effectiveness of each classifier was evaluated using leave-one-out (jack-knife) cross-validation. Interface and non-interface residues were classified with relatively high sensitivity (82.3% and 78.5%) and specificity (81.0% and 77.6%) for proteins in the antigen-antibody and protease-inhibitor complexes, respectively. The correlation between predicted and actual labels was 0.430 and 0.462, indicating that the method performs substantially better than chance (zero correlation). Combined with recently developed methods for identification of surface residues from sequence information, this offers a promising approach to predict residues involved in protein-protein interactions from sequence information alone.
Copyright Owner
Springer-Verlag London Limited
Copyright Date
2004
Language
en
File Format
application/pdf
Recommended Citation
Yan, Changhui; Honavar, Vasant; and Dobbs, Drena, "Identification of interface residues in protease-inhibitor and antigen-antibody complexes: a support vector machine approach" (2004). Genetics, Development and Cell Biology Publications. 116.
https://lib.dr.iastate.edu/gdcb_las_pubs/116
Included in
Bioinformatics Commons, Cell and Developmental Biology Commons, Computational Biology Commons
Comments
This is a manuscript of an article from Neural Computing & Applications 13 (2004): 123. The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-004-0414-3.